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Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>\n            Recommender system helps address information overload problem and satisfy consumers\u2019 personalized requirement in many applications such as e-commerce, social networks, and in-game store. However, existing approaches mainly focus on improving the accuracy of recommendation tasks but usually ignore how to improve the interpretability of recommendation, which is still a challenging and crucial task, especially for some complicated scenarios such as large-scale online games. A few previous attempts on explainable recommendation mostly depend on a large amount of\n            <jats:italic>a priori<\/jats:italic>\n            knowledge or user-provided review corpus, which is labor consuming as well as often suffers from data deficiency. To relieve this issue, we propose a Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation (MHANER) for the case without enough\n            <jats:italic>a priori<\/jats:italic>\n            knowledge or corpus of user comments. Specifically, MHANER employs the attention mechanism to model players\u2019 preference to in-game store items as the support for the explanation of recommendation. Then a graph neural network\u2013based method is designed to model players\u2019 multi-source heterogeneous information, including the players\u2019 historical behavior data, historical purchase data, and attributes of the player-controlled character, which is leveraged to recommend possible items for players to buy. Finally, the multi-level subgraph pattern mining is adopted to combine the characteristics of a recommendation list to generate corresponding explanations of items. Extensive experiments on three real-world datasets, two collected from JD and one from NetEase game, demonstrate that the proposed model MHANER outperforms state-of-the-art baselines. Moreover, the generated explanations are verified by human encoding comprised of hard-core game players and endorsed by experts from game developers.\n          <\/jats:p>","DOI":"10.1145\/3626243","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T12:20:03Z","timestamp":1696854003000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["MHANER: A Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation in Online Games"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8919-1613","authenticated-orcid":false,"given":"Dongjin","family":"Yu","sequence":"first","affiliation":[{"name":"Hangzhou Dianzi University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2843-4831","authenticated-orcid":false,"given":"Xingliang","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9656-6193","authenticated-orcid":false,"given":"Yu","family":"Xiong","sequence":"additional","affiliation":[{"name":"Fuxi AI Lab, NetEase Games, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0447-2614","authenticated-orcid":false,"given":"Xudong","family":"Shen","sequence":"additional","affiliation":[{"name":"Fuxi AI Lab, NetEase Games, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6986-5825","authenticated-orcid":false,"given":"Runze","family":"Wu","sequence":"additional","affiliation":[{"name":"Fuxi AI Lab, NetEase Games, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2152-0446","authenticated-orcid":false,"given":"Dongjing","family":"Wang","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0142-1010","authenticated-orcid":false,"given":"Zhene","family":"Zou","sequence":"additional","affiliation":[{"name":"Fuxi AI Lab, NetEase Games, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-6663","authenticated-orcid":false,"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Broadway, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,7,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CIG.2018.8490456"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186173"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW53433.2021.00009"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-022-01056-9"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/CoG51982.2022.9893595"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186183"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/1326257.1326261"},{"key":"e_1_3_2_9_2","unstructured":"Jennifer Gallup Barbara Serianni Christine Duff and Adam Gallup. 2016. 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